Affinity Based Scheduling Using Bayesian Model and Load Balancing in Multicore Systems

S. Abbasi, S. Kamal
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Abstract

Problems in the shared caches in multicore systems arise due to the non-affinity scheduling. Tasks are scheduled without considering the possible dependencies they have on each other. It has a negative effect on the overall execution time of the tasks. In this paper, we have proposed affinity based scheduling using Bayesian analysis model and creating groups or clusters of dependent tasks. Clusters are then allocated fairly and equally among the multiple cores. Load balancing is performed on the homogeneous system by feeding all the cores in a multicore architecture from a queue-like pool of tasks. We have used another technique for load balancing by defining a chunk size for each core. Results showed an improvement in an overall execution time of a process by 5.57% and of an individual task by 9.06% on average in comparison with other traditional schedulers used by the operating system for a factorial program. For a quick sort program, overall execution time of a process has been reduced by 1.13% while for an individual task by 1.5%.
多核系统中基于贝叶斯模型的亲和性调度与负载均衡
在多核系统中,非亲和性调度导致了共享缓存的问题。任务的调度不考虑它们之间可能存在的依赖关系。它对任务的整体执行时间有负面影响。在本文中,我们提出了基于亲和力的调度使用贝叶斯分析模型和创建组或集群的依赖任务。然后在多个内核之间公平地分配集群。负载平衡是在同构系统上通过从类似队列的任务池中为多核架构中的所有核心提供服务来实现的。我们使用了另一种负载平衡技术,即为每个核心定义一个块大小。结果显示,与操作系统用于析乘程序的其他传统调度器相比,进程的总执行时间提高了5.57%,单个任务的平均执行时间提高了9.06%。对于快速排序程序,进程的总执行时间减少了1.13%,而单个任务的执行时间减少了1.5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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